Encoding and concealing information using deep learning

ABSTRACT

Aspects of the subject disclosure may include, for example, a method for training a deep learning model that includes encoding a content item; generating a blended image by combining a background image and the encoded content; decoding the blended image to generate decoded content corresponding to the content item; and defining or specifying a loss function related to the deep learning model. The method also includes determining values of training parameters for the deep learning model to minimize the loss function, thereby obtaining a trained deep learning model. The method also includes an information concealing procedure using the trained deep learning model to conceal user content by encoding the user content and blending the encoded user content with a user-selected image; the information concealing procedure is substantially independent of the user-selected image. Other embodiments are disclosed.

FIELD OF THE DISCLOSURE

The subject disclosure relates to securing information, and moreparticularly to a system and method for encoding and concealinginformation using deep learning.

BACKGROUND

Many different types of digital information are shared betweenindividuals or via social media (for example: emails, text messages,still images, audio and video). Various formats can be used for thedigital information, and the sources also can vary (for example: printmedia, digitized images and sounds, user-generated video and audio).

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system functioning within the communication network ofFIG. 1 in accordance with various aspects described herein.

FIG. 2B schematically illustrates encoding and concealing informationusing a deep learning model, in accordance with embodiments of thedisclosure.

FIG. 2C schematically illustrates decoding and reconstructing theinformation of FIG. 2B, in accordance with embodiments of thedisclosure.

FIG. 2D depicts an illustrative embodiment of a method for training thedeep learning model of FIGS. 2A-2C, in accordance with various aspectsdescribed herein.

FIG. 2E depicts an illustrative embodiment of a method for using thetrained deep learning model of FIG. 2D to conceal information in abackground image, in accordance with various aspects described herein.

FIG. 2F depicts an illustrative embodiment of encoding and concealingsensitive information relating to an image, in accordance with variousaspects described herein.

FIG. 2G depicts an illustrative embodiment of encoding and concealing awatermark for an item of digital content, in accordance with variousaspects described herein.

FIG. 2H depicts an illustrative embodiment of advertising informationhidden in an image, in accordance with various aspects described herein.

FIG. 2I depicts an illustrative embodiment of hiding faces in anotherimage, in accordance with various aspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for concealing information in an image using a deep learningmodel. In one or more embodiments, the addition of information includingconcealed information can be done according to permissions orauthorizations of user(s) such as opt-ins or other authorizationtechniques. Other embodiments are described in the subject disclosure.

One or more aspects of the subject disclosure include a method thatincludes performing, by a processing system including a processor, atraining procedure for a deep learning model. The training procedureincludes encoding a first content item, thereby generating first encodedcontent; generating a first blended image by combining a firstbackground image and the first encoded content; calculating a firstcalculated loss determined by a first matrix representing the firstbackground image and a second matrix representing the first blendedimage; decoding the first blended image, thereby generating decodedcontent corresponding to the first content item; calculating a secondcalculated loss determined by a third matrix representing the firstcontent item and a fourth matrix representing the decoded content, and aloss function determined in accordance with the first calculated lossand the second calculated loss; and determining values of trainingparameters for the deep learning model to minimize the loss function,thereby obtaining a trained deep learning model. The method alsoincludes performing an information concealing procedure using thetrained deep learning model. The information concealing procedureincludes encoding a second content item, thereby generating secondencoded content; and generating a second blended image by combining asecond background image and the second encoded content; the informationconcealing procedure is substantially independent of the secondbackground image.

One or more aspects of the subject disclosure include a device thatincludes a processing system including a processor, and a memory thatstores executable instructions that, when executed by the processingsystem, facilitate performance of operations. The operations includeperforming a training procedure for a deep learning model. The trainingprocedure includes encoding a first content item, thereby generatingfirst encoded content; generating a first blended image by combining arandomly selected first background image and the first encoded content;calculating a first calculated loss determined by a first matrixrepresenting the first background image and a second matrix representingthe first blended image; decoding the first blended image, therebygenerating decoded content corresponding to the first content item;calculating a second calculated loss determined by a third matrixrepresenting the first content item and a fourth matrix representing thedecoded content, and a loss function determined in accordance with thefirst calculated loss and the second calculated loss; and determiningvalues of training parameters for the deep learning model to minimizethe loss function, thereby obtaining a trained deep learning model. Theoperations also include performing an information concealing procedureusing the trained deep learning model. The information concealingprocedure includes encoding a second content item, thereby generatingsecond encoded content; and generating a second blended image bycombining a second background image and the second encoded content; theinformation concealing procedure is substantially independent of thesecond background image.

One or more aspects of the subject disclosure include a machine-readablemedium comprising executable instructions that, when executed by aprocessing system including a processor, facilitate performance ofoperations. The operations include performing a training procedure for adeep learning model. The training procedure includes encoding a firstcontent item, thereby generating first encoded content; generating afirst blended image by combining a first background image and the firstencoded content; calculating a first calculated loss determined by afirst matrix representing the first background image and a second matrixrepresenting the first blended image; decoding the first blended image,thereby generating decoded content corresponding to the first contentitem; calculating a second calculated loss determined by a third matrixrepresenting the first content item and a fourth matrix representing thedecoded content, and a loss function determined in accordance with thefirst calculated loss and the second calculated loss; and determiningvalues of training parameters for the deep learning model to minimizethe loss function, thereby obtaining a trained deep learning model. Theoperations also include performing an information concealing procedureusing the trained deep learning model. The information concealingprocedure includes encoding a second content item, thereby generatingsecond encoded content; and generating a second blended image bycombining a user-selected second background image and the second encodedcontent; the information concealing procedure is substantiallyindependent of the second background image.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a communications network 100 inaccordance with various aspects described herein. For example,communications network 100 can facilitate in whole or in part encoding acontent item using a deep learning model to generate encoded content,generating a blended image by combining a background image and theencoded content, and decoding the blended image to generate decodedcontent. In particular, a communications network 125 is presented forproviding broadband access 110 to a plurality of data terminals 114 viaaccess terminal 112, wireless access 120 to a plurality of mobiledevices 124 and vehicle 126 via base station or access point 122, voiceaccess 130 to a plurality of telephony devices 134, via switching device132 and/or media access 140 to a plurality of audio/video displaydevices 144 via media terminal 142. In addition, communication network125 is coupled to one or more content sources 175 of audio, video,graphics, text and/or other media. While broadband access 110, wirelessaccess 120, voice access 130 and media access 140 are shown separately,one or more of these forms of access can be combined to provide multipleaccess services to a single client device (e.g., mobile devices 124 canreceive media content via media terminal 142, data terminal 114 can beprovided voice access via switching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system 201 (also referred to herein as DeepEncoding)functioning within the communication network of FIG. 1 in accordancewith various aspects described herein. As shown in FIG. 2A, an inputtext message 210 can be hidden in a background image 211, and thendecoded to yield a decoded text message 218. It is understood that thetype of content of the input is not limited to text, but can be any of awide variety of human-readable and/or machine-convertible content types.Furthermore, it is understood that although the carrying medium for themessage is an image file in FIG. 2A, the disclosure is not so limited;for example, the image concealing the message can be inserted as a frameof a video, printed on paper, etc. In particular, a printed image can beinputted to a device to generate a file that is then decoded to extractthe original message.

DeepEncoding system 201 includes a deep convolutional autoencoder(represented as a convolutional encoder 213 with a convolutional decoder216) using a deep learning model 212. The input text message 210 isencoded to generate image 214, and then combined with the backgroundimage 211 (using a blending function 2121 of the deep learning model) togenerate a blended image 215, with the text hidden in the image. Themodel 212, including the blending function 2121, is trained to limitdistortion of the image; the image distortion 219 is measured by loss₁.The blended image 215 is input to the convolutional decoder 216, whichretrieves the text message and outputs decoded text 218. The model 212is also trained to limit the reconstruction error 217 in the decodedtext; the reconstruction error is measured by loss₂.

FIG. 2B schematically illustrates a procedure 202 for encoding andconcealing information using a deep learning model, in accordance withembodiments of the disclosure. A text message 221 is encoded usingtrained autoencoder model 220 to generate an encoded text image 222.Image 222 is then combined with a background image 225; the backgroundimage may or may not be related to the text message, and may beuser-selected. The resulting blended image 226 includes the hidden text,but to a human reader is virtually indistinguishable from backgroundimage 225.

The autoencoder includes an encoding function ƒ_(e), a decoding functionƒ_(d), and a loss function L. In an embodiment, the autoencoderarchitecture follows the VGG16 model with batch normalization; in otherembodiments, different architectures such as Inception or ResNet may beused. As detailed below, the model is trained with the objective ofminimizing or reducing the loss function L.

FIG. 2C schematically illustrates a procedure 203 for decoding andreconstructing information, in accordance with embodiments of thedisclosure. In an embodiment, the blended image 226 (generated inprocedure 202) is input to convolutional decoder 230; a decoded textimage 231 is output from the decoder. As shown in FIG. 2C, thereconstruction error 232 is determined from the decoded text image 231and the input text image 221.

In this embodiment, one portion of the trained model (either the encoderor the decoder) can be shared publicly, while the other portion of thetrained model is kept secure. In other embodiments, the encoder isaccessible only to particular devices and/or entities, requires a secretkey for access, etc. In additional embodiments, either the encoder orthe decoder can be shared publicly, depending on the informationconcealing being performed. For example, if it is desired to send asecret message to a single recipient, the encoder can be public whilethe decoder is kept secret by the recipient, so that only the recipientcan extract the message. In another example, if it is desired to providea signature or a watermark on artwork, the encoder would be kept private(so that the signature/watermark could be created by only one person)while the decoder can be public (so that anyone can verify thesignature/watermark).

FIG. 2D is a flowchart depicting an illustrative embodiment of a method204 for training the deep learning model of FIGS. 2A-2C, in accordancewith various aspects described herein. In step 2402, the model appliesan encoding algorithm to content (e.g., a text message), generatingencoded content. The encoded content is blended with a background image(step 2404) to obtain a blended image. In this embodiment, thebackground image is randomly selected, and changes with each iterationof the training procedure; the model thus learns to view the backgroundimage as random noise. In this embodiment, an objective of the modeltraining is to ignore the background image while altering it as littleas possible.

The blended image in this embodiment is calculated by an element-wisematrix combination of the encoded text image (for example, image 222)and the background image (for example, image 225). In a case where thedimensions of these two matrices are different, one of the matrices maybe multiplied by a third matrix in order to convert the dimensions,thereby obtaining image matrices with the same dimensions.

The distortion to the background image is measured as loss₁; loss₁ iscalculated using matrix representations of the background image and theblended image (step 2406). In this embodiment, loss₁ is calculated asthe Frobenius norm of the element-wise difference between the matricesrepresenting the background and blended images respectively. In otherembodiments, the difference (or distance) between the matrices can bemeasured using some other method.

The blended image is then decoded (step 2408) to obtain output content.The reconstruction error for the content is measured as loss₂; loss₂ iscalculated using matrix representations of the input content and theoutput content (step 2410). In this embodiment, loss₂ is calculated asthe Frobenius norm between the matrices representing the input andoutput content respectively. In other embodiments, the difference (ordistance) between the matrices can be measured using some other method(e.g., row sum norm, column sum norm).

The loss function L in this embodiment is calculated (step 2412) as aconvex combination of loss₁ and loss₂. Training proceeds with randomlyselected background images (step 2413) with the goal to minimize L (step2414). In this embodiment, early stopping is applied in training themodel parameters to avoid overfitting.

It will be appreciated that since the model 2450 is trained to ignorethe background image, the procedure for concealing information isindependent of the choice of background image. Accordingly, informationcan be hidden in different background images without re-training themodel. A user of the model may therefore personalize the process ofconcealing information by selecting the background image.

FIG. 2E is a flowchart depicting an illustrative embodiment of a method205 for using the trained deep learning model 2450 to conceal userinformation in a background image suitable for sharing with anotheruser, in accordance with various aspects described herein. In step 2502,the model is used to encode a user-defined message and thus generate anencoded message. (The user-defined message may include, withoutlimitation, images, text, and/or any other human-readable ormachine-convertible information.) The model is then used to blend theencoded message with a background image to obtain a blended image (step2504); in an embodiment, the background image is selected by the user,and may or may not be related to the hidden content.

The user may store the blended image (step 2505) and/or send the blendedimage to a recipient (step 2506). In various embodiments, the sender andrecipient of the blended image may be subscribers to an email network, asocial media platform, etc. In an embodiment, the decoder portion of themodel is publicly available and is obtained by the recipient (step2507). The recipient then applies the decoder to the blended image (step2508) to recover the original message.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIGS. 2D and2E, it is to be understood and appreciated that the claimed subjectmatter is not limited by the order of the blocks, as some blocks mayoccur in different orders and/or concurrently with other blocks fromwhat is depicted and described herein. Moreover, not all illustratedblocks may be required to implement the methods described herein.

FIG. 2F depicts an illustrative embodiment 206 of encoding andconcealing sensitive information relating to an image, in accordancewith various aspects described herein. In this embodiment, backgroundimage 241 is an image of a document; private information for anindividual, relating to the document, is included in text image 243. Thetext image is encoded and concealed in blended image 242; the decodedtext appears in image 244. As shown in FIG. 2F, the encoded text ishidden so that the images 241, 242 are virtually indistinguishable. Thereconstruction error of the model causes the decoded text image 244 tohave slightly lower quality than the input text image 243. The decodedtext image 244, when compared with image 243, has small areas 245 wherethe text is blurred but is still easily legible.

In a further embodiment, the text image can include one or more tagsidentifying users belonging to a social network (social tagging). Thetags can be hidden in photographic images shared on the social network.

FIG. 2G depicts an illustrative embodiment 207 of encoding andconcealing a watermark for an item of digital content, in accordancewith various aspects described herein. In this embodiment, digitalcontent (an image 251 of a painting) is provided with a watermark 253.The watermark is encoded and concealed in blended image 252; thereconstructed watermark appears in image 254. As shown in FIG. 2G, theencoded text is hidden so that the images 251, 252 are virtuallyindistinguishable. The decoded watermark 254, when compared with image253, has small blurred areas 255, 256 (resulting from the reconstructionerror in the model) but is still easily legible.

FIG. 2H depicts an illustrative embodiment 208 of advertisinginformation hidden in an image, in accordance with various aspectsdescribed herein. In this embodiment, advertising text 263 is hidden inan image 261; the resulting blended image 262 is virtuallyindistinguishable from image 261. The blended image can be shared withcustomers who can use a publicly available decoder to recover theadvertising in text image 264.

The advertising information may be unrelated, or only tangentiallyrelated, to the image. For example, the image may show a TV program withhidden text advertising a product typically utilized by people who watchthat program. In another embodiment, the background image can be animage from a movie, while the advertising information includessupplemental content (for example, an advertisement for a sequel to themovie, for another movie with the same actors, etc.).

FIG. 2I depicts an illustrative embodiment 209 of hiding a face inanother image, in accordance with various aspects described herein. Inthis embodiment, an image 2911 of a face is encoded and hidden in adifferent image 2912. Decoding the image 2912 yields face image 2913,which is virtually indistinguishable from the original image 2911.Similarly, images 2921, 2931, 2941, 2951, 2961 can be encoded and hiddenin images 2922, 2932, 2942, 2952, 2962 respectively; decoding thoseimages yields respective face images 2923, 2933, 2943, 2953, 2963, whichare virtually indistinguishable from the corresponding original images.Images 2912, 2922, 2932, 2942, 2952 and 2962, while they concealdifferent faces, are virtually indistinguishable from each other.

It will be appreciated that a blended image (e.g. image 226) can be sentfrom one user to another without it being apparent that information isbeing conveyed (as opposed to sending encoded text image 222).Information can thus be concealed and sent while preserving readabilityof the image by the recipient user, but without encryption; in variousembodiments, the encoding portion of the model need be accessible onlyto the sending user (i.e. it is not necessary for the sender andrecipient to exchange keys).

Referring now to FIG. 3, a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular, avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of communicationnetwork 100, the subsystems and functions of system 201, and methods204-205 presented in FIGS. 1, 2A, 2D, and 2E. For example, virtualizedcommunication network 300 can facilitate in whole or in part generatinga blended image by combining a background image and encoded content;calculating a first calculated loss determined by a first matrixrepresenting the background image and a second matrix representing theblended image; decoding the blended image to generate decoded content;and calculating a second calculated loss determined by a third matrixrepresenting the content item and a fourth matrix representing thedecoded content, and a loss function determined in accordance with thefirst calculated loss and the second calculated loss.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 330, 332, 334, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 4, there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 330, 332,334, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 400 canfacilitate in whole or in part encoding a content item using a deeplearning model to generate encoded content, calculating a loss function,and determining values of training parameters for the deep learningmodel in a training procedure to minimize the loss function, therebyobtaining a trained deep learning model.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4, the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5, an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitatein whole or in part determining values of training parameters for a deeplearning model in a training procedure to minimize a loss function,thereby obtaining a trained deep learning model, where an informationsecuring procedure using the trained deep learning model to conceal usercontent by encoding user content and blending the encoded user contentwith a user-selected image is substantially independent of theuser-selected image.

In one or more embodiments, the mobile network platform 510 can generateand receive signals transmitted and received by base stations or accesspoints such as base station or access point 122. Generally, mobilenetwork platform 510 can comprise components, e.g., nodes, gateways,interfaces, servers, or disparate platforms, that facilitate bothpacket-switched (PS) (e.g., internet protocol (IP), frame relay,asynchronous transfer mode (ATM)) and circuit-switched (CS) traffic(e.g., voice and data), as well as control generation for networkedwireless telecommunication. As a non-limiting example, mobile networkplatform 510 can be included in telecommunications carrier networks, andcan be considered carrier-side components as discussed elsewhere herein.Mobile network platform 510 comprises CS gateway node(s) 512 which caninterface CS traffic received from legacy networks like telephonynetwork(s) 540 (e.g., public switched telephone network (PSTN), orpublic land mobile network (PLMN)) or a signaling system #7 (SS7)network 560. CS gateway node(s) 512 can authorize and authenticatetraffic (e.g., voice) arising from such networks. Additionally, CSgateway node(s) 512 can access mobility, or roaming, data generatedthrough SS7 network 560; for instance, mobility data stored in a visitedlocation register (VLR), which can reside in memory 530. Moreover, CSgateway node(s) 512 interfaces CS-based traffic and signaling and PSgateway node(s) 518. As an example, in a 3GPP UMTS network, CS gatewaynode(s) 512 can be realized at least in part in gateway GPRS supportnode(s) (GGSN). It should be appreciated that functionality and specificoperation of CS gateway node(s) 512, PS gateway node(s) 518, and servingnode(s) 516, is provided and dictated by radio technology(ies) utilizedby mobile network platform 510 for telecommunication over a radio accessnetwork 520 with other devices, such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It is should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6, an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 600 can facilitate in whole or in part encoding acontent item using a deep learning model to generate encoded content;generating a blended image by combining a background image and theencoded content; calculating a first calculated loss determined by afirst matrix representing the background image and a second matrixrepresenting the blended image; decoding the blended image to generatedecoded content; and calculating a second calculated loss determined bya third matrix representing the content item and a fourth matrixrepresenting the decoded content, and a loss function determined inaccordance with the first calculated loss and the second calculatedloss.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying acquired cell sites that provide a maximumvalue/benefit after addition to an existing communication network) canemploy various AI-based schemes for carrying out various embodimentsthereof. Moreover, the classifier can be employed to determine a rankingor priority of each cell site of the acquired network. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, . . . ,xn), to a confidence that the input belongs to a class, that is,f(x)=confidence (class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachescomprise, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A method comprising: performing, by a processingsystem including a processor, a training procedure for a deep learningmodel, the training procedure comprising: encoding a first content item,thereby generating first encoded content, generating a first blendedimage by combining a first background image and the first encodedcontent, calculating a first calculated loss determined by a firstmatrix representing the first background image and a second matrixrepresenting the first blended image, decoding the first blended image,thereby generating first decoded content corresponding to the firstcontent item, calculating a second calculated loss determined by a thirdmatrix representing the first content item and a fourth matrixrepresenting the first decoded content, and a loss function determinedin accordance with the first calculated loss and the second calculatedloss, and determining values of parameters for the deep learning modelto minimize the loss function, thereby obtaining a trained deep learningmodel; and performing, by the processing system, an informationconcealing procedure using the trained deep learning model, theinformation concealing procedure comprising: encoding a second contentitem, thereby generating second encoded content, and generating a secondblended image by combining a second background image and the secondencoded content, wherein the information concealing procedure issubstantially independent of the second background image.
 2. The methodof claim 1, wherein the decoding the first blended image comprisesextracting the first encoded content from the first blended image anddecoding the first encoded content, wherein the decoding is performedusing a portion of the deep learning model.
 3. The method of claim 2,wherein the portion of the deep learning model comprises a convolutionaldecoder that is publicly accessible.
 4. The method of claim 1, whereinthe loss function is calculated as a convex combination of the firstcalculated loss and the second calculated loss.
 5. The method of claim1, wherein the generating the first blended image comprises performingan element-wise combination of the first matrix and an encoded contentmatrix representing the first encoded content.
 6. The method of claim 1,wherein the first calculated loss corresponds to a distortion of thefirst background image due to generating the first blended image, andwherein the second calculated loss corresponds to a reconstruction errordetermined by comparing the first content item with the first decodedcontent.
 7. The method of claim 1, wherein in the training procedure thefirst background image is randomly selected.
 8. The method of claim 1,wherein the second content item and the second background image areuser-selected, wherein the generating the second blended image resultsin a secure image concealing the second content item, and wherein theinformation concealing procedure further comprises facilitating sharingthe secure image with a recipient.
 9. The method of claim 8, wherein thesharing is performed via a social network.
 10. The method of claim 1,wherein the first content item and the second content item comprise asame content.
 11. A device, comprising: a processing system including aprocessor; and a memory that stores executable instructions that, whenexecuted by the processing system, facilitate performance of operations,the operations comprising: performing a training procedure for a deeplearning model, the training procedure comprising: encoding a firstcontent item, thereby generating first encoded content, generating afirst blended image by combining a first background image and the firstencoded content, wherein the first background image is randomlyselected, calculating a first calculated loss determined by a firstmatrix representing the first background image and a second matrixrepresenting the first blended image, decoding the first blended image,thereby generating decoded content corresponding to the first contentitem, calculating a second calculated loss determined by a third matrixrepresenting the first content item and a fourth matrix representing thedecoded content, and a loss function determined in accordance with thefirst calculated loss and the second calculated loss, and determiningvalues of parameters for the deep learning model to minimize the lossfunction, thereby obtaining a trained deep learning model; andperforming an information concealing procedure using the trained deeplearning model, the information concealing procedure comprising:encoding a second content item, thereby generating second encodedcontent, and generating a second blended image by combining a secondbackground image and the second encoded content, wherein the informationconcealing procedure is substantially independent of the secondbackground image.
 12. The device of claim 11, wherein the decoding thefirst blended image comprises extracting the first encoded content fromthe first blended image and decoding the first encoded content, whereinthe decoding is performed using a portion of the deep learning model.13. The device of claim 11, wherein the loss function is calculated as aconvex combination of the first calculated loss and the secondcalculated loss.
 14. The device of claim 11, wherein the generating thefirst blended image comprises performing an element-wise combination ofthe first matrix and an encoded content matrix representing the firstencoded content.
 15. The device of claim 11, wherein the firstcalculated loss corresponds to a distortion of the first backgroundimage due to generating the first blended image, and wherein the secondcalculated loss corresponds to a reconstruction error determined bycomparing the first content item with the decoded content.
 16. Anon-transitory machine-readable medium comprising executableinstructions that, when executed by a processing system including aprocessor, facilitate performance of operations, the operationscomprising: performing a training procedure for a deep learning model,the training procedure comprising: encoding a first content item,thereby generating first encoded content, generating a first blendedimage by combining a first background image and the first encodedcontent, calculating a first calculated loss determined by a firstmatrix representing the first background image and a second matrixrepresenting the first blended image, decoding the first blended image,thereby generating decoded content corresponding to the first contentitem, calculating a second calculated loss determined by a third matrixrepresenting the first content item and a fourth matrix representing thedecoded content, and a loss function determined in accordance with thefirst calculated loss and the second calculated loss, and determiningvalues of parameters for the deep learning model to minimize the lossfunction, thereby obtaining a trained deep learning model; andperforming an information concealing procedure using the trained deeplearning model, the information concealing procedure comprising:encoding a second content item, thereby generating second encodedcontent, and generating a second blended image by combining auser-selected second background image and the second encoded content,wherein the information concealing procedure is substantiallyindependent of the second background image.
 17. The non-transitorymachine-readable medium of claim 16, wherein the decoding the firstblended image comprises extracting the first encoded content from thefirst blended image and decoding the first encoded content, wherein thedecoding is performed using a portion of the deep learning model. 18.The non-transitory machine-readable medium of claim 16, wherein the lossfunction is calculated as convex combination of the first calculatedloss and the second calculated loss.
 19. The non-transitorymachine-readable medium of claim 16, wherein the generating the firstblended image comprises performing an element-wise combination of thefirst matrix and an encoded content matrix representing the firstencoded content.
 20. The non-transitory machine-readable medium of claim16, wherein the first calculated loss corresponds to a distortion of thefirst background image due to generating the first blended image, andwherein the second calculated loss corresponds to a reconstruction errordetermined by comparing the first content item with the decoded content.